Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients

Jiayuan Huang; Pengfei Ke; Xiaoyi Chen; Shijia Li; Jing Zhou; Dongsheng Xiong; Yuanyuan Huang; Hehua Li; Yuping Ning; Xujun Duan; Xiaobo Li; Wensheng Zhang; Fengchun Wu; Kai Wu

doi:10.3389/fnagi.2022.823502

Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients

Front Aging Neurosci. 2022 Mar 3:14:823502. doi: 10.3389/fnagi.2022.823502. eCollection 2022.

Authors

Jiayuan Huang^{1

2}, Pengfei Ke¹, Xiaoyi Chen^{1

2}, Shijia Li¹, Jing Zhou¹, Dongsheng Xiong¹, Yuanyuan Huang^{3

4}, Hehua Li^{3

4}, Yuping Ning^{3

4}, Xujun Duan⁵, Xiaobo Li⁶, Wensheng Zhang⁷, Fengchun Wu³, Kai Wu^{2

8

9

10

11

12}

Affiliations

¹ Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.
² School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
³ The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China.
⁴ Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
⁵ MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
⁶ Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
⁷ Institute of Automation, Chinese Academy of Sciences, Beijing, China.
⁸ National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China.
⁹ Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China.
¹⁰ Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.
¹¹ Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
¹² Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.

Abstract

Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20-60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.

Keywords: accelerated brain aging; brain age gap; machine learning; multimodal magnetic resonance imaging; schizophrenia.